The work of Oh et al.[1]}, introduced the innovative “analogy" representation of subtasks.
Here the target objects and the actions which can be applied to a target object are independent.
This makes it possible to share information between tasks by forcing the representation of actions and objects to be independent in the embedding space.
This approach allowed the agent to generalize to unseen combinations of actions and objects.
The work of Bian et al. [2]} addressed a single type of task but focussed on learning different behaviours in different types of environment.
The work of [3]} introduced Compositional Plan Vectors (CPV), which instead of learning the representation of each subtask, the network learns the embedding for a composition of a sequence of sub-tasks. This allows the decomposition of tasks without hierarchical or relational supervision.
Our work bridges these different ideas, learning a latent space where not only can subtasks be composited, but where the skill and knowledge components of subtasks can be shared and permuted. This makes it possible to solve never before seen task combinations, as a step towards zero-shot IL and general AI.
